Method of mapping and machine learning for patient-healthcare encounters to predict patient health and determine treatment options

Abstract

A health evaluator is configured to determine future patient health. The health evaluator receives pertinent health data regarding a patient and analyzes the pertinent health data by a health prediction machine learning model trained on baseline health data to generate predictive health data regarding an expected patient condition. The baseline health data is based at least in part on electronic medical records (EMRs). The health evaluator generates treatment information for the patient based on one or more sets of the predictive health data generated by the health evaluator.

1 . A method of predicting future patient health, the method comprising:
 receiving, by a machine learning model trained to identify a future parameter status of a subject health parameter based on baseline health data, pertinent health data regarding a patient, wherein the machine learning model is implemented on a health evaluator having memory and control circuitry, and wherein the baseline health data is generated based on sets of features extracted from sets of electronic medical records of each patient of a patient population associated with the subject health parameter;   analyzing the pertinent health data, the machine learning model, to generate predictive health data regarding an expected patient condition;   generating, by the health evaluator, treatment information for the patient based on one or more sets of the predictive health data; and   outputting, by the health evaluator, the treatment information.   
     
     
         2 . The method of  claim 1 , further comprising:
 modifying, by the health evaluator, the pertinent health data to generate modified health data; and   analyzing the modified health data, by the machine learning model, to generate the predictive health data regarding the expected patient condition.   
     
     
         3 . The method of  claim 2 , further comprising:
 generating the pertinent health data based on patient data and treatment data.   
     
     
         4 . The method of  claim 2 , further comprising:
 identifying first factors of the pertinent health data as modifiable; and   modifying one or more of the first factors to generate the modified health data.   
     
     
         5 . The method of  claim 2 , wherein:
 generating, by the health evaluator, the treatment information for the patient based on the one or more sets of the predictive health data includes:
 identifying, by the health evaluator, a best fit set of the predictive health data based on a count of predicted diagnoses for each set of the predictive health data; and 
   outputting, by the computing device, the treatment information includes:
 outputting, by the computing device, the best fit set of the predictive health data as the treatment information. 
   
     
     
         6 . The method of  claim 1 , wherein analyzing the pertinent health data, by the machine learning model, to generate the predictive health data regarding the expected patient condition includes:
 comparing, by the health evaluator, the generated predictive health data and control data regarding the patient; and   determining, by the health evaluator, a correlation between the generated predictive health data and target patient health based on the comparison.   
     
     
         7 . The method of  claim 6 , further comprising:
 modifying the pertinent health data to generate a plurality of sets of modified health data;   generating a plurality of additional sets of predictive health data based on the plurality of sets of modified health data;   comparing, by the health evaluator, each set of the plurality of additional sets of predictive health data and the control data;   determining, by the health evaluator, correlations between each set of the plurality of additional sets of predictive health data and the target patient health based on the comparison between each set of the plurality of additional sets of predictive health data and the control data.   
     
     
         8 . The method of  claim 7 , further comprising:
 applying, by the health evaluator, a scoring metric to each set of predictive health data to generate scored sets of predictive health data.   
     
     
         9 . The method of  claim 8 , wherein outputting, by the computing device, the treatment information includes identifying, by the health evaluator, a best fit one of the scored sets of predictive health data based on the scoring metric. 
     
     
         10 . The method of  claim 9 , wherein identifying, by the health evaluator, the best fit one of the scored sets of predictive health data based on the scoring metric includes:
 generating, by the health evaluator, a composite score for each set of predictive health data forming the scored sets of predictive health data; and   identifying, by the health evaluator, the best fit one of the scored sets of predictive health data based on the composite scores.   
     
     
         11 . The method of  claim 8 , further comprising:
 generating, by the health evaluator, a composite score for each set of predictive health data forming the scored sets of predictive health data; and   ranking, by the health evaluator, the scored sets of predictive health data based on the composite scores for each set of predictive health data forming the scored sets of predictive health data.   
     
     
         12 . The method of  claim 1 , wherein analyzing the pertinent health data, by the machine learning model, to generate the predictive health data regarding the expected patient condition, comprises:
 generating, by the machine learning model, predicted laboratory results for the patient based on the pertinent health data; and   generating, by the machine learning model, predicted diagnoses based on the predicted laboratory results and the pertinent health data.   
     
     
         13 . The method of  claim 12 , further comprising:
 generating, by the machine learning model, predicted health actions based on the predicted diagnoses, the predicted laboratory results, and the pertinent health data; and   building the treatment information based on the predicted health actions.   
     
     
         14 . The method of  claim 2 , wherein analyzing the pertinent health data, by the machine learning model, to generate the predictive health data regarding the expected patient condition, comprises:
 generating, by the machine learning model, predicted laboratory results based on the modified health data; and   generating, by the machine learning model, predicted diagnostic data as at least a portion of the predicted health data, wherein the predicted diagnostic data includes one or more expected morbidities of the patient based on the predicted laboratory results and the pertinent health data.   
     
     
         15 . The method of  claim 14 , further comprising:
 comparing, by the health evaluator, the predicted diagnostic data and control data to determine whether the predictive health data corresponds with target patient health, wherein the control data is pre-generated and provided to the health evaluator, and wherein the control data is based on treatment goals for the patient.   
     
     
         16 . The method of  claim 15 , wherein comparing, by the health evaluator, the predicted diagnostic data and the control data to determine whether the predictive health data corresponds with the target patient health comprises:
 comparing, by the health evaluator, a diagnosis count of the predicted diagnostic data with a target diagnosis count of the control data; and   determining, by the health evaluator, that the predictive health data corresponds with the target patient health based on the diagnosis count of the predicted diagnostic data being one of equal to and less than the target diagnosis count of the control data.   
     
     
         17 . A method of generating future health information, the method comprising:
 (i) receiving pertinent health data, including vital signs and electronic medical records, associated with a subject patient;   (ii) setting initial design variables representing modifiable risk factors associated with the subject patient;   (iii) predicting laboratory results associated with the patient, by a machine learning model trained on baseline health data that is generated based on sets of electronic medical records of patients in a patient population, based on the received pertinent health data and the initial design variables;   (iv) generating predictive health data for the subject patient, by the machine learning model, based on the predicted laboratory results and the pertinent health data;   (v) comparing, by a health evaluator having control circuitry and memory and configured to implement the machine learning model, the predictive health data and control data to determine whether the predictive health data corresponds with desired patient health; and   (vi) outputting the predictive health data as treatment information based on the health evaluator determining that the predictive health data corresponds with desired patient health.   
     
     
         18 . The method of  claim 17 , wherein generating predictive health data for the patient, by the machine learning predictive model, based on the predicted laboratory results and the pertinent health data, comprises:
 generating predicted diagnostic data for the patient, by the machine learning model, based on the predicted laboratory results.   
     
     
         19 . The method of  claim 17 , further comprising:
 introducing modifications to the initial design variables to generate modified design variables; and   iteratively repeating steps (iii)-(vi) based on the modified design variables to generate a plurality of sets of predictive health data.   
     
     
         20 . The method of  claim 19 , wherein iteratively repeating steps (iii)-(vi) based on the modified design variables to generate the plurality of sets of predictive health data includes:
 iteratively repeating steps (iii)-(vi) based on an iteration threshold; and   stopping continued simulation based on the modified design variables based on an iteration count reaching or exceeding the iteration threshold.